Evaluation method of rowing performance and its optimization for UAV-based shot seeding device on rice sowing

Liu, Wei; Zhou, Zhiyan; Xu, Xuelang; Gu, Qingyu; Zou, Shuaishuai; He, Weizhuo; Luo, Xiwen; Huang, Junhao; Lin, Jianqin; Jiang, Rui

doi:10.1016/j.compag.2023.107718

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…During the operation, the UAV was seeded according to the set parameters. During the sowing process, the real-time weight of the storage tank displayed on the ground station [22,23].…”

Section: Test Device and Circuit Designmentioning

confidence: 99%

“…During the operation, the UAV was seeded according to the set parameters. During the sowing process, the real-time weight of the storage tank displayed on the ground station [22,23]. The experimental setup used a two-cantilever-beam weighing sensor (Yongkang Jushi Weighing Instrument Co., Ltd., Yongkang, China) monitoring storage tank, and its technical information as shown in Table 1.…”

Section: Test Device and Circuit Designmentioning

confidence: 99%

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Quantity Monitor Based on Differential Weighing Sensors for Storage Tank of Agricultural UAV

Huang,

He,

Yang

et al. 2024

Drones

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Nowadays, unmanned aerial vehicles (UAVs) play a pivotal role in agricultural production. In scenarios involving the release of particulate materials, the precision of quantity monitors for the storage tank of UAVs directly impacts its operational accuracy. Therefore, this paper introduces a novel noise-mitigation design for agricultural UAVs’ quantity monitors, utilizing differential weighing sensors. The design effectively addresses three primary noise sources: sensor-intrinsic noise, vibration noise, and weight-loading uncertainty. Additionally, two comprehensive data processing methods are proposed for noise reduction: the first combines the Butterworth low-pass filter, the Kalman filter, and the moving average filter (BKM), while the second integrates the Least Mean Squares (LMS) adaptive filter, the Kalman filter, and the moving average filter (LKM). Rigorous data processing has been conducted, and the monitor’s performance has been assessed in three UAV typical states: static, hovering, and flighting. Specifically, compared to the BKM, the LKM’s maximum relative error ranges between 1.24% and 2.74%, with an average relative error of 0.31%~0.58% when the UAV was in a hovering state. In flight mode, the LKM’s maximum relative error varies from 1.68% to 10.06%, while the average relative error ranges between 0.74% and 2.54%. Furthermore, LKM can effectively suppress noise interference near 75 Hz and 150 Hz. The results reveal that the LKM technology demonstrated superior adaptability to noise and effectively mitigates its impact in the quantity monitoring for storage tank of agricultural UAVs.

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“…During the operation, the UAV was seeded according to the set parameters. During the sowing process, the real-time weight of the storage tank displayed on the ground station [22,23].…”

Section: Test Device and Circuit Designmentioning

confidence: 99%

Section: Test Device and Circuit Designmentioning

confidence: 99%

Quantity Monitor Based on Differential Weighing Sensors for Storage Tank of Agricultural UAV

Huang,

He,

Yang

et al. 2024

Drones

Self Cite

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“…To address these issues, this study aims to leverage the advantages of UAVs for seedling throwing operations. UAV operations are characterized by rapid execution speed and excellent maneuverability [12]. Using UAVs allows for liberation from the structural constraints inherent in ground-based machinery.…”

Section: Introductionmentioning

confidence: 99%

Design and Experimentation of Rice Seedling Throwing Apparatus Mounted on Unmanned Aerial Vehicle

Yuan,

Yang,

Wei

et al. 2024

Agriculture

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In order to further exploit the production advantages of rice throwing, this paper proposes a systematically designed throwing device suitable for integration with unmanned aerial vehicles (UAVs). The device primarily comprises a seedling carrying and connection system, a seedling pushing mechanism, and a seedling guiding device. The operational principles and workflow of the device are initially elucidated. Subsequently, an analysis of factors influencing rice throwing effectiveness is conducted, with throwing height, working speed, and the bottom diameter of the seedling guide tube identified as key factors. Seedling spacing uniformity and seedling uprightness are designated as performance indicators. A three-factor, three-level response surface experiment is conducted, yielding regression models for the experimental indicators. Through an analysis of the response surface, the optimal parameter combination is determined to be a throwing height of 142.79 cm, a working speed of 55.38 r/min, and a bottom diameter of the seedling guide tube of 43.51 mm. At these parameters, the model predicts a seedling spacing uniformity of 88.43% and a seedling uprightness of 88.12%. Field experiments validate the accuracy of the optimized model results. Experimental data indicate that under the optimal operational parameters calculated by the regression model, the seedling spacing uniformity is 86.7%, and the seedling uprightness is 84.2%. The experimental results meet the design requirements, providing valuable insights for UAV rice-throwing operations.

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“…With the rapid development of artificial intelligence, scholars have achieved excellent results in seed recognition tasks by employing Convolutional Neural Networks (CNNs) [15][16][17] . In traditional pattern recognition methods, identification conditions are manually set.…”

Section: Introductionmentioning

confidence: 99%

Recognition model for coated red clover seeds using YOLOv5s optimized with an attention module

Zhang,

Xuan,

Hou

2023

International Journal of Agricultural and Biological Engineering

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The non-destructive recognition of coated seeds is crucial for advancing studies in coating theory. Currently, the recognition of coated seeds heavily relies on manual visual inspection and machine vision detection. However, these methods pose challenges such as high misclassification rates, low recognition efficiency, and elevated labor intensity. In response to the aforementioned challenges, this study leveraged deep learning techniques to develop a coated seed recognition model named YOLO-Coated Seeds Recognition (YOLO-CSR), aiming to address the challenges posed by coated seed recognition tasks. The experiment of this study mainly includes the following steps: First, a seed coating machine was set up to coat red clover seeds, resulting in three types of coated red clover seeds. Subsequently, by collecting images of the three types of coated seeds, a coated seed image dataset was further constructed. Then, the YOLOv5s was built, incorporating the Convolutional Block Attention Module (CBAM) into the model's backbone to enhance its ability to learn features of coated seeds. Finally, the training results of YOLO-CSR were compared with those of other classical recognition models. The experimental results showed that YOLO-CSR achieved the best recognition performance on the self-built coated seed image dataset. The average precision (AP) for recognizing the three types of coated seeds reached 98.43%, 97.91%, and 97.26%, with a mean average

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Evaluation method of rowing performance and its optimization for UAV-based shot seeding device on rice sowing

Cited by 9 publications

References 6 publications

Quantity Monitor Based on Differential Weighing Sensors for Storage Tank of Agricultural UAV

Quantity Monitor Based on Differential Weighing Sensors for Storage Tank of Agricultural UAV

Design and Experimentation of Rice Seedling Throwing Apparatus Mounted on Unmanned Aerial Vehicle

Recognition model for coated red clover seeds using YOLOv5s optimized with an attention module

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